GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 21, GB2002, doi:10.1029/2005GB002634, 2007
Changes in biomass carbon stocks in China’s grasslands between
1982 and 1999
Shilong Piao,1 Jingyun Fang,1 Liming Zhou,2 Kun Tan,1 and Shu Tao1
Received 7 October 2006; revised 10 October 2006; accepted 12 December 2006; published 11 April 2007.
[1] Terrestrial ecosystems in the northern latitudes are significant carbon sinks for
atmospheric CO2; however, few studies come from grassland ecosystems. Using national
grassland resource inventory data, NDVI (normalized difference vegetation index)
time series data set, and a satellite-based statistical model, this study identifies changes
in the size and distribution of aboveground biomass carbon (C) stocks for China’s
grasslands between 1982 and 1999. Biomass C stocks averaged 145.4 Tg C for
the study period for a total area of 334.1 104 km2, and have increased by 17.7 Tg C
(1 Tg = 1012 g) from 136.3 Tg C in the early 1980s (average of 1982–1984) to
154.0 Tg C in the late 1990s (average of 1997–1999), with an annual increase of 0.7%.
This suggests that the aboveground biomass of China’s grasslands may have functioned
as the C sinks in the past 2 decades. Assuming a constant ratio of aboveground to
belowground biomass for each grassland type, we also estimated belowground biomass C
and its change over time for each grassland type, generating an average estimate of
1051.1 Tg C for the total (aboveground and belowground) biomass C and an annual
increase of 126.67 Tg C for China’s grasslands over the 18 years. However, the
accuracy of these estimates has limitations due primarily to uncertainties in estimates
of belowground C, biomass inventories, and satellite time series data sets.
Citation: Piao, S., J. Fang, L. Zhou, K. Tan, and S. Tao (2007), Changes in biomass carbon stocks in China’s grasslands between
1982 and 1999, Global Biogeochem. Cycles, 21, GB2002, doi:10.1029/2005GB002634.
1. Introduction
[2] Terrestrial ecosystems of the Northern Hemisphere
have been a significant carbon (C) sink in the past several
decades [Schimel et al., 2001], but such evidence comes
mainly from forest ecosystems [Pacala et al., 2001; Myneni
et al., 2001; Fang et al., 2001, 2005; Goodale et al., 2002;
Shvidenko and Nilsson, 2002]. Several studies have suggested that the widely distributed grasslands in the northern
latitudes may be a potential C sink [Tate et al., 1995;
Scurlock and Hall, 1998; Batjes, 1998; Janssens et al.,
2003; Jacobs et al., 2003], but the evidence is very limited
[Hall and Scurlock, 1991; Hall et al., 1995]. Grassland is
one of the most widespread vegetation types worldwide,
occupying about 1/5 of the world’s land surface [Hall et al.,
1995; Scurlock and Hall, 1998]. Accordingly, accurate
evaluation of the global C cycle requires a precise understanding of the magnitude and spatial distributions of grasslands’ C stocks and their changes.
[3] Grassland covers nearly 1/3 of China’s total territory
and is experiencing notable effects of anthropogenic activ1
Department of Ecology, College of Environmental Science and Key
Laboratory for Earth Surface Processes of the Ministry of Education,
Peking University, Beijing, China.
2
School of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, Georgia, USA.
Copyright 2007 by the American Geophysical Union.
0886-6236/07/2005GB002634
ities [Department of Animal Husbandry Veterinary, 1996;
Chen and Wang, 2000]. Quantifying changes in China’s
grassland biomass C stocks is critical not only for precise
evaluation of spatial distribution and changes of China’s
terrestrial C stocks, but also for the sustainable use of
China’s grassland resources [Ni, 2002].
[4] Developments in the field of remote sensing have
greatly increased the opportunity for monitoring and
detecting vegetation and land cover changes [Defries and
Townshend, 1994; Myneni et al., 2001; Schino et al., 2003].
The normalized difference vegetation index (NDVI), which
measures the contrast between red and near-infrared reflection of solar radiation, can reflect vegetation photosynthetic activity, and is therefore often used to estimate
vegetation biomass [Paruelo et al., 1997, 2004; Paruelo
and Lauenroth, 1998; Myneni et al., 2001; Dong et al.,
2003; Piao et al., 2005a]. Compared with its application in
the estimation of forest biomass, NDVI data have more
potential in the estimation of leaf area index (LAI) and thus
green biomass of grasslands. This is because the NDVI does
not saturate in most grasslands due to a low LAI, as it does
in dense forest canopy [Asner et al., 2004]. Accordingly, the
application of satellite data to the estimation of grassland
biomass and net primary production (NPP) has been conducted worldwide [e.g., Paruelo et al., 1997, 2004; Holm et
al., 2003; Di Bella et al., 2004; Brogaard et al., 2005].
[5] Several studies have documented a positive relationship between NDVI derived from National Oceanic and
GB2002
1 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) and either biomass or
aboveground NPP for different grassland ecosystems [e.g.,
Burke et al., 1991; Prince, 1991; Hobbs, 1995; Paruelo et
al., 1997, 2004; Holm et al., 2003; Di Bella et al., 2004]. In
this study, we are to integrate China’s grassland resource
inventory and NDVI time series data to construct the
relationship between the AVHRR-NDVI and aboveground
biomass for China’s grasslands and then to estimate magnitude, distribution and changes of the biomass C stocks for
China’s grassland between 1982 and 1999.
2. Data Sets and Methods
2.1. Grassland Resource Inventory and
Grassland Map
[6] China has conducted the first national grassland
resource inventory from 1981 to 1988. The inventory aimed
at investigating types, distribution, area, and forage yield of
grasslands and carrying capacity of rangelands all over
China [Department of Animal Husbandry Veterinary,
1996]. The field measurement relevant with forage yield
followed the following standards [North Region Office
for Grassland Resources Inventory, 1986; Department of
Animal Husbandry Veterinary, 1996].
2.1.1. Definition of Grassland
[7] The grassland inventoried refers to the primary and
secondary grasslands with a vegetation cover of 5%,
shrubby grasslands with a shrub cover of <40%, alpine
thickets suitable for grazing, and seeded grasslands.
2.1.2. Locating Inventory Site
[ 8 ] Topographical maps with a scale of 1:50,000,
1:100,000 and 1:200,000 (depending on grassland types)
were used to locate inventory sites. The resolution of the
inventory sites was set at 50 – 100 km2 across all the
inventoried area (95% of the country territory and
>2000 counties).
2.1.3. Plot Size and Data Record
[9] Each inventory site contained 4 – 10 plots of varying
size depending on grassland types: 1 m2 for typical grasslands, 4 m2 for desert grasslands, 0.5 m2 for meadows, and
100 m2 for tall herbaceous and shrubby grasslands. For each
plot, community height, coverage, abundance, and life form
were recorded for dominant and subdominant species.
2.1.4. Measurement of Forage Yield
[10] Fresh forage weight was measured in the field at the
period with annual peak standing yield with >3 replicates
for each plot, and air-dried and weighed in the lab.
2.1.5. Data Error and Representative
[11] The error of area and forage yield was controlled at
<1% and <10%, respectively. The inventoried grassland
type required high representative: for each county, at least
2 inventory sites for each grassland type and at least 3 plots
for each grassland type were surveyed.
[12] The grassland resource inventory has provided a
powerful data source for estimating China’s grassland
biomass C stocks. The aboveground biomass data used in
the present study were documented from forage yield
reported by Department of Animal Husbandry Veterinary
[1994] which recorded detailed information on area, forage
GB2002
yield, and carrying capacity of rangelands for each grassland type for each province.
[13] Information on the distribution of grassland types
was extracted from the Map of Grassland Resources in
China at 1: 4, 000, 000 scale [Commission for Integrated
Survey of Natural Resources, 1996]. On the basis of this
map, China’s grasslands were grouped into 17 types
[Department of Animal Husbandry Veterinary, 1996; Chen
and Fischer, 1998]. Figure 1 and Table 1 present the details
for these grassland types.
2.2. NDVI and Climate Data Set
[14] The NDVI data used were from the Global Inventory
Monitoring and Modeling Studies (GIMMS) group derived
from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/
AVHRR) Land data set, at a spatial resolution of 8 8 km2
and 15-day interval, for the period January 1982 to December 1999 [Tucker et al., 2001; Zhou et al., 2001]. The data
set has been modified to minimize the effects of volcanic
eruptions, solar zenith angle, sensor degradation and other
factors, and thus has been carefully optimized [Slayback et
al., 2003] and been widely used for large-scale studies of
ecological processes [Myneni et al., 2001; Tucker et al., 2001;
Zhou et al., 2001; Fang et al., 2003; Piao et al., 2003]. The
NDVI in sparsely vegetated areas is influenced by the
spectral characteristics of the soil, and the signal associated
with the vegetation can be difficult to separate from that of
the background [Huete, 1989]. Hence, similar to Zhou et al.
[2001] and Myneni et al. [2001], we only analyzed areas
with annual mean NDVI > 0.1 unit in this study.
[15] In order to match remote sensing data with the
average grassland forage yield obtained from the grassland
resource inventory, we first converted the provincial map of
China and the grassland map of China to a grid format at
8 8 km2 resolution from original coverage format using
ARCINFO 7.1.1 software. We then overlaid the NDVI
images during the period of 1982 – 1988 over a provincial
map of China and the grassland map of China, and thus
obtained average NDVI corresponding to locations of each
grassland type for each province.
[16] Climatic data used in this study included monthly
precipitation and temperature records for 1982 – 1999,
which were produced from 680 well-distributed climate
stations across China [Piao et al., 2003]. We generated
growing seasonal (April to October) temperature and precipitation data from the monthly climate records in order to
explore possible relationships between grassland biomass
and climate variables.
2.3. Relationship Between Aboveground Biomass C
and NDVI and Error Analysis for Biomass Estimates
[17] Department of Animal Husbandry Veterinary [1994]
have provided detailed information on average forage yield
(air-dried weight, or aboveground yield, kg ha1) by grassland type by province in China (except Taiwan). Aboveground biomass (dry weight mass) was calculated using
forage yield and water content for each grassland type
[Fang et al., 1996]. Aboveground biomass was converted
2 of 10
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
GB2002
GB2002
Figure 1. Spatial distribution of 17 grassland types in China (based on Department of Animal
Husbandry Veterinary [1996] data). These types were grouped on the basis of climatic zonation, humidity
index, vegetation type of grassland, and their importance in livestock husbandry [Department of Animal
Husbandry Veterinary, 1996; Chen and Fischer, 1998]. Among the grassland types, temperate steppe,
temperate desert-steppe, temperate steppe-desert, and temperate deserts are mainly distributed in arid and
semiarid areas in northern China. High-cold meadow steppe, high-cold steppe, high-cold desert steppe,
and alpine meadows appear in the Tibetan Plateau. Tropical grassland, tropical shrub-grassland, warmtemperate grassland, and warm-temperate shrub-grassland occur primarily in the eastern part of temperate
and subtropical zones.
Table 1. Average Aboveground Biomass C Stock for the Early 1980s, the Late 1990s, and Averaged Over 18 Years, and the Trend and
Relative Trend of the C Stock Over the Year and Their p Value, From 1982 to 1999, for Each Grassland Type and Country Total in Chinaa
Aboveground C Stock, Tg C
Grassland Type
Area,
104 km2
Early
1980s
Late
1990s
1982 – 1999
Trend,
TgC yr1
Relative
Trend, % yr1
r (p Value)
Uncertainty,
%
Temperate meadow-steppe
Temperate steppe
Temperate desert-steppe
High-cold meadow-steppe
High-cold steppe
High-cold desert steppe
Temperate steppe-desert
Temperate desert
High-cold desert
Tropical grassland
Tropical shrub-grassland
Warm-temperate grassland
Warm-temperate shrub-grassland
Lowland meadow
Temperate montane meadow
Alpine meadow
Marsh
Country total
16.21
42.47
17.80
6.50
45.97
8.33
8.11
27.10
4.85
10.90
14.75
3.89
7.05
25.88
17.41
73.10
1.10
334.1
9.99
16.34
3.28
1.35
8.04
0.88
1.18
3.85
0.48
8.58
10.90
2.57
4.83
14.24
12.45
36.69
0.71
136.3
11.19
18.95
3.71
1.54
8.87
0.96
1.32
4.84
0.52
9.57
12.35
2.96
5.64
16.08
13.98
40.75
0.78
154.0
10.57
17.49
3.49
1.46
8.48
0.92
1.24
4.36
0.50
9.08
11.60
2.79
5.27
15.23
13.30
38.86
0.75
145.4
0.072
0.166
0.031
0.011
0.049
0.005
0.011
0.058
0.003
0.046
0.069
0.018
0.039
0.102
0.085
0.244
0.004
1.01
0.69
0.95
0.88
0.74
0.57
0.50
0.85
1.32
0.52
0.50
0.59
0.66
0.75
0.67
0.64
0.63
0.60
0.7
0.73 (0.001)
0.66 (0.003)
0.49 (0.039)
0.54 (0.021)
0.50 (0.035)
0.42 (0.084)
0.48 (0.046)
0.67 (0.002)
0.44 (0.071)
0.47 (0.047)
0.50 (0.033)
0.45 (0.062)
0.52 (0.027)
0.82 (0.000)
0.72 (0.001)
0.69 (0.002)
0.82 (0.000)
0.79 (0.000)
41.1
47.1
25.9
56.0
60.8
78.0
29.6
29.6
101.6
34.8
22.9
23.1
29.1
36.4
25.9
48.1
41.4
35.9
a
Early 1980s are average of 1982 – 1984, late 1990s are average of 1997 – 1999, and average over 18 years are for 1982 – 1999. Error evaluation
(uncertainty) and coefficient of correlation (r) are also indicated. The grid cells with a monthly mean NDVI of < 0.1 are excluded from data analysis to
eliminate impact of bare and sparsely vegetated grids on the NDVI trend.
3 of 10
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
GB2002
GB2002
Figure 2. Relationship between aboveground biomass C density (C stock per area) and growing season
NDVI of grasslands in China. The C density values are annual peak standing mass for the period 1981 –
1988 for each grassland type by each province, and NDVI values are 7-year (1982 to 1988) averaged
growing season NDVI.
into units of C, using a conversion factor of 0.45 [Lieth and
Whittaker, 1975].
[18] Because forage yield reported by Department of
Animal Husbandry Veterinary [1994] was measured during
the period 1981 – 1988, we calculated growing season
average NDVI for each pixel for the same period to develop
the regressive relationship between aboveground biomass C
and growing season NDVI (equation (1) and Figure 2). We
then used equation (1) and NDVI to estimate aboveground
biomass C for each pixel for different periods.
Y ¼ 291:64 NDVI 1:5842
2
R ¼ 0:72; P < 0:001 ;
ð1Þ
where Y is aboveground biomass C per area (g C m2).
[19] To evaluate the errors of the biomass estimates
derived from the statistic model, we compared predicted
values with inventory observations by grassland type using
the following equation [Smith et al., 1997]:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
100 X
RMSE ¼ ðPi Oi Þ2 =n;
O
ð2Þ
i¼1
where RMSE is root mean square error for grassland type, Pi
is averaged
and Oi are the predicted and observed values, O
observed values, and n is number of samples.
2.4. Estimation of Belowground Biomass
[20] In grassland ecosystems, a large portion of biomass C
is stored below ground [Coupland, 1992]. In order to
identify changes in total (above- and below-ground) biomass C stocks for China’s grasslands over the past
2 decades, it is necessary to estimate the changes in
belowground biomass C stocks. To estimate this change,
we assumed a constant ratio of belowground to aboveground biomass C for each grassland type through the study
period and collected these average ratio for major grassland
types (Table 2).
[21] The approach of the aboveground vs. belowground
biomass ratio has been the most common method for
estimating belowground biomass at different scales from
local to landscape, region and biome [Intergovernmental
Panel on Climate Change, 2003; Mokany et al., 2006].
Physiologically the ratio is explained as reflecting the
differential investment of photosynthates between the
aboveground and belowground organs [Titlyanova et al.,
1999; Mokany et al., 2006]. However, because of a strong
effect by the accumulated dead biomass over time, this
functional explanation is often questioned [Klepper, 1991].
Therefore Shackleton et al. [1988] suggested that the
interpretation based on the fine root vs. leaf biomass ratio
is much reasonable. In addition, as stated hereafter, the
Table 2. Ratios of Belowground to Aboveground Biomass for
17 Grassland Types in China
Grassland Type
Ratio
Reference
Temperate meadow-steppe
Temperate steppe
Temperate desert-steppe
Temperate steppe-desert
Temperate desert
Temperate montane meadow
5.26
6.76
10.08
7.89
7.89
6.23
Warm-temperate grassland
Warm-temperate shrub- grassland
Tropical grassland
Tropical shrub-grassland
High-cold meadow steppe
High-cold desert
High-cold steppe
High-cold desert steppe
Alpine meadow
Lowland meadow
3.50
3.80
2.01
0.45
7.91
7.89
9.43
7.89
7.92
6.31
Marsh
15.68
Fang et al. [1996]
Wu [2002]
Wu [2002]
Wu [2002]
Wu [2002]
Department of
Animal Husbandry
Veterinary [1994]
Wu [2002]
Wu [2002]
Wu [2002]
Wu [2002]
Li et al. [1998]
Li et al. [1998]
Wu [2002]
Li et al. [1998]
Li et al. [1998]
Department of
Animal Husbandry
Veterinary [1994]
Department of
Animal Husbandry
Veterinary [1994]
4 of 10
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
GB2002
GB2002
Figure 3. Interannual variations in aboveground biomass C for China’s grassland over the period of
1982 – 1999.
approach has several other limitations; for example, the
ratio may be influenced by the relatively long turnover time
of the structural roots and the climate change [Gill et al.,
2002; Hui and Jackson, 2006]. Despite these limitations,
our study still applies this approach to estimate belowground biomass, as many previous studies have been done
[e.g., Jackson et al., 1996; Gill et al., 2002; Mokany et al.,
2006]. This is because the spatial variation (across different
places and thus different climate, soil, and topography) in
the ratios seems be much larger than its year-to-year change
[Mokany et al., 2006], and because the focus of our study is
on the estimation of aboveground biomass and its pattern at
the biome and national scales.
3. Results
3.1. Interannual Changes in Aboveground Biomass
Carbon Stocks
[22] Using equation (1) and growing season average
NDVI data, we calculated average aboveground biomass
C stock for the early 1980s (average of 1982– 1984), the
late 1990s (average of 1997– 1999), and the 18-year overall
average for each grassland type and country total (Table 1).
Over the 18 years, aboveground biomass C stocks averaged
145.4 Tg C yr1 over a total area of 334.1 104 km2, and
significantly increased from 136.3 Tg C in the early 1980s
to 154.0 Tg C in the late 1990s (r = 0.79, p < 0.001), with a
net increase of 17.7 Tg C or an annual increase of 1.01 Tg C
(annual rate of 0.7%) (Figure 3).
[23] The C stock by grassland type also tended to increase, but the increasing rate differed among the grassland
types (Table 1). High-cold desert steppe, high-cold desert
and warm-temperate grassland all showed a slight increase
(p > 0.05) and the rest was significant (p < 0.05). The
largest annual increase was observed in alpine steppe, with
0.244 Tg C yr1, which accounted for approximately 24%
of the total annual C increase. Following this, temperate
steppe, lowland meadow, and temperate montane meadow,
contributed to 16%, 10%, and 8% of the total annual C
increase, respectively. In addition, temperate desert steppe
had the largest relative annual increase rate of 1.32% among
all grassland types, while tropical grassland and high-cold
desert steppe had the smallest value (0.50%).
[24] The error analysis suggests a difference of 35.9%
between the model- and inventory-based estimates of the
country-level biomass C stock. For the biome level (grassland type), the errors showed a large difference; alpine
grassland types (high-cold desert, high-cold desert steppe,
and high-cold steppe) were of a large value (56% to 101.6%),
while that for temperate meadow-steppe, temperate steppe,
tropical grassland, lowland meadow, alpine meadow was
between 30% and 50%, and others below 30%.
3.2. Spatial Distribution and Changes of Aboveground
Biomass C Stocks
[25] Figure 4 illustrates the spatial distribution and frequency distribution of the 18-year averaged C stock per area
(aboveground biomass C density). A large spatial heterogeneity was exhibited; high C density appeared in eastern Tibet,
eastern Inner Mongol (western slope of Mts. Daxinganling), and western and central parts of Mts. Tian-shan, while
small C stock occurred in east and central Inner Mongol, and
western and central Tibet (Figure 4a). Overall, the biomass C
density ranged from 5 g C.m2 to 125 g C.m2, concentrating
on a value of 10 –90g C m2 (being 85% of total study area)
(Figure 4b).
[26] Figure 5a illustrates the spatial distribution of the
difference in aboveground biomass C density between the
late 1990s (an average of 1997– 1999) and the early 1980s
(an average of 1982 – 1984); the positive value represents an
increase in C stock per area (C sink), while the minus value
implies a decrease in C stock per area (C source). The
statistics indicated that about 70% of the total study area
has increased and the rest 30% decreased over the past
2 decades. The largest C increase (>20 g C m2 yr1)
occurred in the northern side of Mts. Tian-shan, the oasis
5 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
GB2002
Figure 4. The 18-year averaged aboveground biomass C density (C stock per area) during 1982 – 1999
in China. (a) Spatial distribution of biomass C density. A high C density appears in Eastern Tibet
(location A), eastern Inner Mongol (western slope of Mts. Daxinganling) (location B), and western and
central parts of Mts. Tian-shan (location C), while small C stock is in east and central Inner Mongol
(location D), and western and central Tibet (location E). (b) Frequency distribution of biomass C density.
around the Taklimakan Desert, and Mts. Altai in northwest
China, Songnen Plain in Northeast China, and eastern Hulun
Buir Plateau and parts of the Horqin Sand Land, Inner
Mongolia. The significant C decrease (<5 g C m2)
appeared mainly along the Qing-zang highway area (a major
road from Xining, Qinghai to Lhasa, Tibet) in the east Tibetan
Plateau, possibly resulting from a human-induced degradation
of grassland vegetation.
[27] The frequency distribution of the differences in
biomass C density between the two periods of the late
1990s and the early 1980s suggested that about 50% of
the total study area did not change (5.05.0 g C m2)
and 35% and 8% increased by 5 – 15 g C m2and 15–
30 g C m2, respectively, while only about 2% decreased
by >5 g C m2 (Figure 5b).
4. Discussion
4.1. Changes in Aboveground and Belowground
Biomass C Stocks
[28] Aboveground biomass C stock of China’s grassland
averaged 145.4 Tg C and has increased by 17.7 Tg C from
6 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
GB2002
Figure 5. Difference between aboveground biomass C density (C stock per area) in the late 1990s (an
average of 1997 – 1999) and in the early 1980s (an average of 1982– 1984) over 1982– 1999 in China.
(a) Spatial distribution of this difference. The difference of the C stocks expresses size of the C increase
(C sink) or decrease (C source); the positive value means the C increase, while minus value implies the
C decrease. (b) Frequency distribution of this difference.
136.3 Tg C in the early 1980s to 154.0 Tg C in the late
1990s, with an annual increasing rate of 0.7% over the
period of 1982 to 1999. Such a significant increase corresponded to pronounced climate changes over the study area
(Figure 6). Growing season average temperature for the
study area increased by 0.052°C per year (r = 0.69, p =
0.002) and growing season precipitation also tended to
increase (0.77 mm/yr), but was not significant at the 5%
significance level (r = 0.17, p = 0.478). Statistical analysis
revealed that the total mean C density was positively related
with both growing season average temperature (r = 0.79,
P = 0.0001) and growing season precipitation (r = 0.48, P =
0.04). Because the positive trends in C density, temperature, and precipitation may cause a spurious correlation
among the variables, we used the method proposed by
Zhou et al. [2001] to calculate the relationship between
C density and the detrended growing season temperature and
precipitation. The results showed that a positive and significant correlation at 5% significance level was between the
C density and detrended temperature (r = 0.57, p = 0.018)
and precipitation (r = 0.55, p = 0.019), suggesting that
increased temperature may be associated with increasing
7 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
Figure 6. Changes in growing season average temperature
and precipitation for China’s grassland area over the period
of 1982 – 1999.
C stocks and interannual changes in precipitation may be
a factor in the fluctuations of C stocks.
[29] Generally, an increase in temperature promotes vegetation productivity primarily through two ways. First,
enhanced soil nitrogen (N) mineralization and availability
caused by an increase in temperature would stimulate
plant N uptake and thus lead to increased NPP and vegetation growth [Melillo et al., 2002]. Second, rising temperature would result in extension of growing season length,
and eventually increase in plant photosynthesis [White
et al., 1999]. Remote sensing data indicate that over the
last 2 decades, growing season length of temperate grassland, deserts, and alpine meadow and tundra in China
has extended by about 1.1 day yr1, 2.3 day yr1 and
0.9 day yr1, respectively [Piao et al., 2006]. In addition,
atmospheric CO2 fertilization effect [Polley, 1997] and
human activity such as land management may also partly
account for the observed increase in biomass C stocks of
China’s grassland.
[30] As described above, a large portion of biomass C is
stored below ground in grassland ecosystems [Coupland,
1992]. We used a constant ratio of aboveground versus
belowground biomass through the study period for each
grassland type (Table 2) to estimate the total country
belowground biomass as 851.5 Tg C in the early 1980s
(average of 1982– 1984) and 960.5 Tg C in the late 1990s
(average of 1997 – 1999). As a result, the total biomass C
stock of China’s grasslands averaged 1051.1 Tg C during
the past 2 decades and increased from 987.8 Tg C in the
early 1980s to 1114.5 Tg C in the late 1990s, with an overall
increase of 126.7 Tg C, or an annual increase of 7.04 Tg C
GB2002
(an increase rate of 0.7% annually). This estimate compares
favorably with that derived from a carbon cycle model,
CASA (Carnegie-Stanford-Ames Approach) [Potter et
al., 1993]. The CASA uses NDVI, temperature, precipitation, solar radiation, and soil type and texture to estimate
vegetation net primary production (NPP) [Potter et al., 1993;
Field et al., 1995]. The CASA-estimated annual NPP
increased by 7.2 Tg C yr1 for China’s grasslands (temperate
grassland, high land vegetation and savanna) from 1982 to
1999 [Fang et al., 2003; Piao et al., 2005b], which was very
close to the estimate of 7.04 Tg C yr1 described above.
[31] Note that, like many others [e.g., Scurlock et al.,
2002], we used maximum annual biomass as a proxy of
NPP in this estimation, although there are several limitations of the peak biomass method. Previous studies have
showed that the peak biomass method is only suitable for
temperate grassland with short growing season, and may
underestimate NPP for tropical grasslands [Long et al.,
1989; Scurlock et al., 2002]. However, such an effect on
country-level aboveground NPP change may be negligible
because of a very small area of tropical grassland in China
(about 7.6% of total grassland area).
[32] Our estimates suggest that China’s grasslands may
have acted as a biomass C sink over the past 2 decades, but
this sink is much smaller than that of forests. Fang et al.
[2001] reported that China’s forest has sequestrated 21 Tg
of C annually during the last 2 decades, which is about
3 times of that of China’s grassland. Considering that the
total area of grasslands is about 3 times that of forests in
China (331.4 104 km2 for grasslands versus 105.54 104 km2 for forests), the biomass C sink strength of
grasslands is only 1/9 of that of forests.
[33] According to Olson et al. [1983] and Prentice et al.
[1993], the area of global grasslands is 4160 104 5155 104 km2, with a total biomass C stock of 27.9 50.4 Pg.
The global grassland C sink was estimated as 0.5 Pg C yr1
[Scurlock and Hall, 1998]. Applying China’s estimate of
biomass C sink per area (7.04 Tg C yr1/331 104 km2)
and the increase rate (percentage) per biomass (0.7%/yr
over the 18 years) in our study, global grassland biomass C
sink could be 88– 109 Tg C yr1 and 0.20 – 0.36 Pg C yr1
in the past 2 decades, respectively. Both values are lower
than the global C sink estimate by Scurlock and Hall
[1998], although their estimate included the soil C sequestration. The discrepancy between these two estimates may
be attributed to two factors: (1) a relatively smaller biomass
C sink of China’s grasslands may underestimate the global
grassland C absorption, and (2) soil C sequestration for
grasslands may be critical to increased terrestrial C sink but
is neglected in this analysis. The first factor may be true
because China’s grassland biomass C per area was less than
half of the world average due to anthropogenic activities
such as overgrazing [Chen and Wang, 2000], but for the
second one, it remains uncertain and requires further study.
4.2. Uncertainties of Estimate of Biomass C Stocks
[34] In this study, we have explored the biomass C stocks
and their changes for China’s grasslands over the past
2 decades, but its size still remains uncertain due to an
uncertainty of grassland resource inventory data, satellite
8 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
time series dataset, and belowground C stocks. Unlike
forests with a relatively low-frequency variation, grasslands are very sensitive to environmental changes, and
hence show fast and large interannual changes in biomass
C stocks resulting from natural events and human activities [Lauenroth and Sala, 1992]. Thus here we assume that
(1) any standing dead matter or litter was carried from
previous year, (2) death in current year is negligible, (3) live
biomass was not from previous year, and (4) ratio of aboveground to belowground C is dependent on grassland type,
and does not vary with environmental change [Scurlock et
al., 2002]. This may influences the analyses of the temporal and spatial patterns of regional biomass C stocks,
particularly in the regions with intensively grazed or cut
grasslands where biomass C stocks may be underestimated,
but may not exert a significant effect on the difference
between biomass C stocks in different periods, for example,
trend of biomass C stocks over the study period. Furthermore, although the grassland resource inventory was well
designed and its sampling sites were across whole the
country, uncertainties still exist in the inventory data, mainly
coming from measurement errors and sampling errors.
[35] Another possible uncertainty is from the NDVI time
series data set. Although every effort has been paid to NDVI
corrections, some variations from the satellite drift and
changeover and incomplete corrections for calibration loss
and atmospheric effects may still remain in the NDVI
dataset. This is always a challenge for the application of
satellite data in ecological study [Slayback et al., 2003].
[36] Finally, the largest error for the estimation of the
large-scale biomass C stocks for whole grassland ecosystem
should be induced from the belowground biomass. Due to
technical and methodological limitations, the contribution
of belowground C stock to the regional and global C cycle
remains largely unquantified [Copley, 2000; Gill et al.,
2002]. Our study used the constant ratios of aboveground
to belowground C by grassland type to estimate the belowground biomass C, and thus probably reduces precision of
our estimates. For example, belowground root sampling
generally includes both live and dead roots, and thus how to
eliminate the effect of dead roots is always a challenge for
the assessment of a real ratio; the relatively long turnover
time of the structural roots suggest that the allocation of
grassland production cannot arrive to an equilibrium in a
few years [Mokany et al., 2006]; the allocation of plant
production generally varies with the climate change [Gill et
al., 2002; Hui and Jackson, 2006]. Despite these limitations, this approach practically is still applied in most of the
recent studies [e.g., Gill et al., 2002; Mokany et al., 2006],
because at this moment we do not have better methods to
estimate belowground biomass [Scurlock et al., 2002;
Intergovernmental Panel on Climate Change, 2003]. In
fact, interannual change in the ratios seems to be considerably small within an ecosystem, and its spatial variation
(across different places and thus different climate, soil, and
topography) might be much larger than that in different
years [Mokany et al. 2006]. Although we do not have data
to test this assumption, a study from the US tallgrass prairie
suggested that the ratio did not change significantly over
time [Knapp et al., 1998]. A 15-year observation in Siberian
GB2002
grasslands also showed an unclear trend of the ratios
although there were large seasonal changes [Titlyanova et
al., 1999]. Overall, estimating belowground biomass of
grassland ecosystems based on available data sources and
reasonable methodology is a big challenge for accurately
evaluating biomass C stocks of grasslands and require
future study.
[37] In closure, while an increase in peak biomass for
grasslands over the period indicates a stimulation of initial
C sequestration in the ecosystem, we still do not know
whether China’s grassland ecosystems are carbon sink or
source in the past 2 decades, which is dependent on the
balance of changes in both plant and soil carbon pools. If
the increases of biomass carbon storage exceed that of soil
carbon transfers to the atmosphere through carbon decomposition, net carbon sequestration would occur, and eventually the whole ecosystem would function as a carbon sink.
In our present study, we focus on the quantification of
changes in grassland biomass carbon stocks but not for
those of the whole ecosystem (biomass, litterfall and soil
organic carbon). To quantify the changes in whole ecosystem carbon stocks for China’s grasslands, additional data on
geographical and interannual variability in soil carbon
storage are greatly needed.
[38] Acknowledgments. We thank D. Flynn and B. Zhu for assistance in preparing the manuscript. We also thank two reviewers for their
constructive comments. This study was supported by the National Natural
Science Foundation of China (grants 40024101, 40638039, and 40152003)
and Peking University.
References
Asner, G. P., D. Nepstad, G. Cardinot, and D. Ray (2004), Drought stress
and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy, Proc. Natl. Acad. Sci. U. S. A., 101, 6039 – 6044.
Batjes, N. H. (1998), Mitigation of atmospheric CO2 concentrations
by increased carbon sequestration in the soil, Biol. Fertil. Soils, 27,
230 – 235.
Brogaard, S., M. Runnstrom, and J. W. Seaquist (2005), Primary production
of Inner Mongolia, China, between 1982 and 1999 estimated by a satellite data-driven light use efficiency model, Global Planet. Change, 45,
313 – 332.
Burke, I. C., et al. (1991), Regional analysis of the Central Great Plains,
Bioscience, 41, 685 – 692.
Chen, Y. F., and G. Fischer (1998), A new digital georeferenced database of
grassland in China, Interim Rep. IR-98-062, Int. Inst. for Appl. Syst.
Anal., Laxenburg, Austria.
Chen, Z. Z., and S. P. Wang (2000), Typical Steppe Ecosystems of China,
Sci. Press, Beijing.
Commission for Integrated Survey of Natural Resources (1996), Map of
grassland resources in China, 1:4M, map, Sci. Press, Beijing.
Copley, J. (2000), Ecology goes underground, Nature, 406, 452 – 454.
Coupland, R. T. (1992), Mixed prairie, in Grasslands of the World, edited
by R. T. Coupland, pp. 151 – 182, Cambridge Univ. Press, New York.
Defries, R. S., and J. R. G. Townshend (1994), NDVI-derived land cover
classification at a global scale, Int. J. Remote Sens., 15, 3567 – 3586.
Department of Animal Husbandry and Veterinary (1994), Data on Grassland Resources of China, pp. 10 – 75, China Agric. Sci. and Technol.
Press, Beijing, China.
Department of Animal Husbandry and Veterinary (1996), Rangeland Resources of China, China Agric. Sci. and Technol. Press, Beijing, China.
Di Bella, C., et al. (2004), Remote sensing capabilities to estimate pasture
production in France, Int. Remote Sens, J., 25, 5359 – 5372.
Dong, J., et al. (2003), Remote sensing estimates of boreal and temperate
forest woody biomass: Carbon pools, sources and sinks, Remote Sens.
Environ., 84, 393 – 410.
Fang, J. Y., G. H. Liu, and S. L. Xu (1996), Carbon pools in terrestrial
ecosystems in China, in Emissions and Their Relevant Processes of
Greenhouse Gases in China, edited by G. C. Wang and Y. P. Wen,
pp. 109 – 128, Chin. Environ. Sci. Publ., Beijing.
9 of 10
GB2002
PIAO ET AL.: CARBON STOCKS IN CHINA’S GRASSLANDS
Fang, J. Y., et al. (2001), Changes in forest biomass carbon storage in China
between 1949 and 1998, Science, 292, 2320 – 2322.
Fang, J. Y., et al. (2003), Increasing net primary production in China from
1982 to 1999, Front. Ecol. Environ., 1, 293 – 297.
Fang, J. Y., T. Oikawa, T. Kato, W. H. Mo, and Z. H. Wang (2005),
Biomass carbon accumulation by Japan’s forests from 1947 to 1995,
Global Biogeochem. Cycles, 19, GB2004, doi:10.1029/2004GB002253.
Field, C. B., J. T. Randerson, and C. M. Malmstrom (1995), Global net
primary production: Combing ecology and remote sensing, Remote Sens.
Environ., 51, 74 – 88.
Gill, R. A., et al. (2002), Using simple environmental variables to estimate
below-ground productivity in grasslands, Global Ecol. Biogeogr., 11,
79 – 86.
Goodale, C. L., et al. (2002), Forest carbon sinks in the Northern Hemisphere, Ecol. Appl., 12, 891 – 899.
Hall, D. O., and J. M. O. Scurlock (1991), Climate change and productivity
of natural grasslands, Ann. Bot., 67, suppl., 49 – 55.
Hall, D. O., et al. (1995), Response of temperate and tropical grasslands to
CO2 and climate change, J. Biogeogr., 22, 537 – 547.
Hobbs, T. J. (1995), The use of NOAA-AVHRR NDVI data to assess
herbage production in the arid rangelands of Central Australia, Int. J.
Remote Sens., 16, 1289 – 1302.
Holm, A. M., S. W. Cridland, and M. L. Roderick (2003), The use of timeintegrated NOAA NDVI data and rainfall to assess landscape degradation
in the arid shrubland of Western Australia, Remote Sens. Environ., 85,
145 – 158.
Huete, A. P. (1989), Soil influences in remotely sensed vegetation-canopy
spectra, in Theory and Application of Optical Remote Sensing, edited by
G. Asrar, pp. 107 – 141, John Wiley, Hoboken, N. J.
Hui, D., and R. B. Jackson (2006), Geographical and interannual variability
in biomass partitioning in grassland ecosystems: A synthesis of field data,
New Phytol., 169, 85 – 93.
Intergovernmental Panel on Climate Change (2003), Good Practice
Guidance for Land Use, Land-Use Change and Forestry, Geneva,
Switzerland.
Jackson, R. B., et al. (1996), A global analysis of root distributions for
terrestrial biomes, Oecologia, 108, 389 – 411.
Jacobs, A. F. G., B. G. Heusinkveld, and A. A. M. Holtslag (2003), Carbon
dioxide and water vapour flux densities over a grassland area in the
Netherlands, Int. J. Climatol., 23, 1663 – 1675.
Janssens, I. A., et al. (2003), Europe’s terrestrial biosphere absorbs 7 to 12%
of European anthropogenic CO2 emissions, Science, 300, 1538 – 1542.
Klepper, B. (1991), root-shoot relationships, in Plant Roots: The Hidden
Half, edited by Y. Waisel, A. Eshel, and U. Kafkafi, p. 265 – 286, CRC
Press, Boca Raton, Fla.
Knapp, A. K., J. M. Briggs, D. C. Hartnett, and S. L. Collins (1998),
Grassland Dynamics: Long-Term Ecological Research in Tallgrass
Prairie, Oxford Univ. Press, New York.
Lauenroth, W. K., and O. E. Sala (1992), Long-term forage production of
North American shortgrass steppe, Ecol. Appl., 2, 397 – 403.
Li, W. H., et al. (1998), Biomass of ecosystems on the Tibetan Plateau, in
Ecosystems of Qinghai-Xizang (Tibetan) Plateau and Approach for Their
Sustainable Management, edited by W. H. Li and X. M. Zhou, pp. 56 – 58,
Guangdong Sci. and Technol. Press, Guangzhou, China.
Lieth, H., and R. H. Whittaker (1975), Primary Productivity of the Biosphere, Springer, New York.
Long, S. P., et al. (1989), Primary productivity of natural grass ecosystems
of the tropics: A reappraisal, Plant Soil, 115, 155 – 166.
Melillo, J. M., et al. (2002), Soil warming and carbon cycle feedbacks to the
climate systems, Science, 298, 2173 – 2176.
Mokany, K., R. John, and A. S. Prokushkin (2006), Critical analysis of root:
Shoot ratios in terrestrial biomes, Global Change Biol., 12, 84 – 96.
Myneni, R. B., et al. (2001), A large carbon sink in the woody biomass of
northern forests, Proc. Natl. Acad. Sci. U. S. A., 98, 14,784 – 14,789.
Ni, J. (2002), Carbon storage in grasslands of China, J. Arid Environ., 50,
205 – 218.
North Region Office for Grassland Resources Inventory (1986), Technical
Standards for Grassland Resources Inventory, China Agric. Sci. and
Technol. Press, Beijing.
Olson, J. S., J. A. Watts, and L. J. Allison (1983), Carbon in Live Vegetation of Major World Ecosystem, pp. 50 – 51, Oak Ridge Natl. Lab., Oak
Ridge, Tenn.
Pacala, S. W., et al. (2001), Consistent land- and atmosphere-based US
carbon sink estimates, Science, 292, 2316 – 2320.
Paruelo, J. M., and W. K. Lauenroth (1998), Interannual variability of
NDVI and its relationship to climate for North American shrublands
and grasslands, J. Biogeogr., 25, 721 – 733.
GB2002
Paruelo, J. M., et al. (1997), ANPP estimates from NDVI for the Central
Grassland Region of the United States, Ecology, 78, 953 – 958.
Paruelo, J. M., et al. (2004), Regional scale relationships between ecosystem structure and functioning: The case of the Patagonian steppes, Global
Ecol. Biogeogr., 13, 385 – 395.
Piao, S. L., et al. (2003), Interannual variations of monthly and seasonal
NDVI in China from 1982 to 1999, J. Geophys. Res., 108(D14), 4401,
doi:10.1029/2002JD002848.
Piao, S. L., J. Y. Fang, B. Zhu, and K. Tan (2005a), Forest biomass carbon
stocks in China over the past 2 decades: Estimation based on integrated
inventory and satellite data, J. Geophys. Res., 110, G01006, doi:10.1029/
2005JG000014.
Piao, S. L., J. Y. Fang, L. M. Zhou, B. Zhu, K. Tan, and S. Tao (2005b),
Changes in vegetation net primary productivity from 1982 to 1999 in
China, Global Biogeochem. Cycles, 19, GB2027, doi:10.1029/
2004GB002274.
Piao, S. L., et al. (2006), Variations in satellite-derived phenology in Chinas
temperate vegetation, Global Change Biol., 12, 672 – 685.
Polley, H. W. (1997), Implications of rising atmospheric carbon dioxide
concentration for rangelands, J. Range Manage., 50, 561 – 577.
Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek,
H. A. Mooney, and S. A. Klooster (1993), Terrestrial ecosystem production: a process model based on global satellite and surface data, Global
Biogeochem. Cycles, 7, 811 – 841.
Prentice, I. C., et al. (1993), Modelling global vegetation patterns and
terrestrial carbon storage at the last glacial maximum, Global Ecol. Biogeogr. Lett., 3, 67 – 76.
Prince, S. D. (1991), Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981 – 1988, Int. J. Remote
Sens., 12, 1301 – 1311.
Schimel, D. S., et al. (2001), Recent patterns and mechanisms of carbon
exchange by terrestrial ecosystems, Nature, 414, 169 – 172.
Schino, G., et al. (2003), Satellite estimate of grass biomass in a mountainous range in central Italy, Agrofor. Syst., 59, 157 – 162.
Scurlock, J. M. O., and D. O. Hall (1998), The global carbon sink: A
grassland perspective, Global Change Biol., 4, 229 – 233.
Scurlock, J. M. O., K. Johnson, and R. J. Olson (2002), Estimating net
primary productivity from grassland biomass dynamics measurements,
Global Change Biol., 8, 736 – 753.
Shackleton, C. M., B. McKenzie, and J. E. Granger (1988), Seasonal
changes in root biomass, root/shoot ratios and turnover in two coastal
grassland communities in Transkei, S. Afr. J. Bot., 54, 465 – 471.
Shvidenko, A., and S. Nilsson (2002), Dynamics of Russian forests and the
carbon budget in 1961 – 1998: An assessment based on long-term forest
inventory data, Clim. Change, 55, 5 – 37.
Slayback, D., J. Pinzon, S. Los, and C. J. Tucker (2003), Northern Hemisphere photosynthetic trends 1982 – 1999, Global Change Biol., 9, 1 – 15.
Smith, P., et al. (1997), A comparison of the performance of nine soil
organic matter models using datasets from seven long-term experiments,
Geoderma, 81, 153 – 225.
Tate, K. R., A. Parsholtam, and D. J. Ross (1995), Soil carbon storage and
turnover in temperate forests and grasslands—A New Zealand perspective, J. Biogeogr., 22, 695 – 700.
Titlyanova, A. A., I. P. Romanova, N. P. Kosykh, and N. P. MironychevaTokareva (1999), Pattern and process in aboveground and belowground
components of grassland ecosystems, J. Veg. Sci., 10, 307 – 320.
Tucker, C. J., et al. (2001), Higher northern latitude Normalized Difference
Vegetation Index and growing season trends from 1982 to 1999, Int. J.
Biometeorol., 45, 184 – 190.
White, M. A., S. W. Running, and P. E. Thornton (1999), The impact of
growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest, Int. J. Biometeorol., 42, 139 – 145.
Wu, H. B. (2002), Changes in carbon storage of terrestrial ecosystems in
China since the Late Glacial Maximum, Ph.D. dissertation, Inst. of Geol.
and Geophys., Chin. Acad. of Sci., Beijing.
Zhou, L. M., et al. (2001), Variations in northern vegetation activity inferred
from satellite data of vegetation index during 1981 to 1999, J. Geophys.
Res., 106, 20,069 – 20,083.
J. Fang (corresponding author), S. Piao, K. Tan, and S. Tao, Department
of Ecology, College of Environmental Sciences, Peking University, Beijing
100871, China. (jyfang@urban.pku.edu.cn)
L. Zhou, School of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, GA 30332-0340, USA.
10 of 10